An ecrm model for banking industry In Iran

New Marketing Research Journal Special Issue, May 2012 pp: 1-16 An ecrm model for banking industry In Iran Mohammad Hossein Ronaghi ISC Research Affairs Islamic World Science Citation Center, Shiraz, Iran Mh_ronaghi@yahoo.com Alireza Dehdarizadeh Administrative Dep. Regional information Center for science & technology, Shiraz, Iran a.dehdarizadeh@ricest.ir Sina Safaee ISC Research Affairs Islamic World Science Citation Center, Shiraz, Iran Sina.safaee@gmail.com Amin Asadpoor Administrative Dep. Regional information Center for science & technology, Shiraz, Iran Asadpoor2631@yahoo.com Abstract ecrm (electronic Customer Relationship Management) systems focus on using the web-site as the main interaction channel for businesses to simulate an old fashioned one-to-one direct relationship high touch with customers. The purpose of this paper is to propose a model for the relationship between ecrm and E-loyalty by conceptualizing that e-loyalty is influenced by e-satisfaction and e-trust. To examine the model a descriptive survey was applied. The survey sample population consisted of 513 randomly taken e-banking service users in the city of Tehran, according to a cluster random sampling skim. An important part of the results revealed Trust beliefs relate positively with satisfaction and loyalty among e- banking customers. Keywords: ecrm, e-satisfaction, e-loyalty, e-trust, e-banking services.

2 / New Marketing Research Journal, Special Issue, May 2012 Introduction Technology and the internet are having a profound effect on marketing. One area particularly affected has been the automation of customer service or e- service. Mean while internet access among people in nearly all parts of the world rapidly continues to grow [1]. Since the Internet is a new distribution channel with enormous potential, there is a strong need to explore the impact of ecrm on online services in business, commerce and banking as well as to explore the factors influencing consumer s purchasing [2]. The success of electronic commerce is determined in part by whether consumers trust sellers and their commodities they cannot see, touch or examine. Without trust, it is difficult to imagine a transactional relationship could be developed or maintained [3]. Trust helps to ensure that one party will not take advantage of the vulnerability of the other during or after a transaction. Lack of consumer s trust is a critical obstacle to the success of transactions [4]. Privatization, globalization and fast deregulation constantly create new challenges in Iran s commerce and banking activities. Banking industry scenario in Iran has recently been reviewed to cope with emerging tremendous demand for more effective banking services. Experts in banking industry believe providing e-banking services can help to overcome some of the difficulties. But e-business growth in our country is relatively slow. Lack of customer s trust has its own share in the slowness. The fact that makes worthy to study on customer s trust in e-banking services. This study investigates consumer s trust attitude and Satisfaction towards online services in ecrm of banking industry. Before analyzing the hypothesis and a designed conceptual model for ecrm in Iranian banking industry, we began to review the existing literature on ecrm dimensions and some specified developed models. The concept of CRM Customer Relationship Management (CRM) systems can help organizations manage customer interactions more effectively. Like many new technologies, CRM has been accompanied by vendor hype and stories of implementation failure. Work on critical success factors (CSFs) should encourage more appropriate implementation practice; however many CSF studies conclude with a list of factors but provide little further guidance. CRM has developed as an approach based on maintaining positive relationships with customers, increasing customer loyalty, and expanding customer lifetime value. Understanding the needs of customers and offering value-added services are recognized as factors that determine the success or failure of companies [5]. Trust:trust is a quite challenging concept to define, because it manifests itself in many different forms [6]. Most researchers have defined trust according to their specific disciplinary worldview. Psychologists define trust as a tendency to trust others. Social psychologists define trust as cognition about the trustier. Sociologists define trust as a characteristic of the institutional environment. Many variants of the above types exist,

An ecrm model for banking industry In Iran / 3 including a significant number in e- commerce research [7]. We first need to form a clear idea of just what trust is. Literature that fails to take this step often ends up causing further confusion and debate amongst researchers rather than adding to the knowledge base. The lack of a widely accepted definition has been highlighted by numerous researchers [8], but most clearly in Hosmer (1995), where it was stated that there appears to be widespread agreement on the importance of trust in human conduct, but unfortunately there also appears to be equally widespread lack of agreement on a suitable definition of the concept. The aim of this study focuses solely on the consumer s perceptions of online trust in the electronic commerce environment between the consumer and the online vendor. The focus is on developing a framework to explain online trust that can be used in electronic commerce (individual buyer) contexts. For this study, the definition of online trust is adapted from Mayer et al. (1995) online trust is the willingness of a consumer to be vulnerable to the actions of an online service/store based on the expectation that the online service/store will perform a particular action important to the consumer, irrespective of their ability to monitor or control the online service/store. This definition seems generally enough to encompass trust for almost all types of operations and transactions. It is specifying the realm and implicative meaning of the online trust in the electronic commerce context in several ways. First, across disciplines trust is normally more discussed in an uncertain and risky environment. Also online trust is more under consideration in uncertain or vulnerable situations controlled by online stores. Second, it is assumed to be a kind of expectancy or predictability. It cannot exist without some possibility of error or failure. Third, online trust requires mutuality. There must be a trusting party (trustier) and a party to be trusted (trustee) in any trust relationship. Fourth, online trust is related to good (or nonnegative) outcomes. Factors contributing to trust In ecrm contexts, the diversity in trust conceptualization is also evident [9]. Prior studies have viewed trust as (I) trust beliefs [10] or (II) trusting intentions [11, 12]. Trusting intentions: Trusting intentions means the trustier is securely willing to depend, or intends to depend, on the trustee [7]. Trusting Beliefs: Trusting beliefs mean the confident trustier's perception that the trustee-in this context, a specific web-based service has attributes that are beneficial to the trustier. Three trusting beliefs are utilized most often [13]: competence (ability of the trustee to do what the trustier needs), benevolence (trustee caring and motivation to act in the trustier interests), and Integrity (trustee's honesty and promise keeping). Because the difference between many of the types was small, we used conceptual clustering to group them into categories. Then we picked the three that appeared most frequently and seemed most relevant (competence, benevolence and Integrity were obtained).

4 / New Marketing Research Journal, Special Issue, May 2012 Loyalty: Loyalty is a historical word, rooted in feudal times when allegiance to the sovereign was fundamental to the success, perhaps even the survival of the state. The Oxford Dictionary s definition of the word "loyal" is: True or faithful (to duty, love or obligation), steadfast in allegiance, devoted to the legitimate sovereign or government of one s country. Oliver (1999) defines brand loyalty as a deeply held commitment to repurchase a preferred product/service consistently in the future, thereby causing repetitive purchasing of samebrand or same brand-set, despite existence of situational influences and marketing efforts, potentially enough to cause switching behavior. In the context of e-commerce or online banking services, loyalty is most often defined simply as the intention of a consumer to repurchase products/services through a particular e- service provider. Satisfaction: Satisfaction, according to Oliver (1997) is the summary psychological state resulting when the emotion surrounding disconfirmed expectations are coupled with a consumer s prior feelings about the consumer experience. From his perspective, satisfaction may be best understood as an ongoing evaluation of the surprise inherent in a product/service acquisition and/or consumption experience. In this research, e-satisfaction is defined as the contentment of the customer with respect to his or her prior purchasing experience with a given electronic commerce firm. Customer satisfaction can be divided into 7 factors [14]: 1-"Customer Support" refers to customer services, feedback, and responsiveness, which can build satisfaction for future purchases. 2-"Security" refers to the extent to which a website protects customer s transaction data and privacy from interception or misuse. 3-"Ease of Use" means the usability of a website s user interface. 4-"Digital Products/Services" includes the core item and the entire package of offerings, such as the design and quality of the digital products or services. 5-"Transaction and Payment" is characterized by the payment systems and transaction procedure offered by a web site. 6-"Information Content" involves the information quality (e.g., accuracy and relevancy) provided by the sales force or the online site. 7-"Innovation" refers to the ability of a web site to provide innovative products and timely Information. ecrm Models Chen, Wang, and Jiang s study (2009) focused on the relationship between knowledge and trust. The results indicated that, the more users know about online shopping, the more they trust in online shopping; the more they trust, the more they shop online. This was a very simple and useful result. Hence, consumer education is important for electronic commerce; since consumers will not be afraid of shopping online if they know more about Internet security (See figure 1).

An ecrm model for banking industry In Iran / 5 Know ld Trust i Onlin e Percei d Trust Fig.1: Chen's model [3] Sulaiman, Mohezar and Rasheed s study (2007) shows two independent variables which include trust worthiness of e-vendors and trust propensity. Distrust in e-commerce transaction is determined as the dependent variable. Perceived security control, perceived privacy control, perceived integrity and perceived competence are four constructs proposed to measure the trustworthiness of e-vendors. Personality, cultural environment and experience on the other hand are used to measure the trust propensity (See figure 2). Trustworthiness of E-vendors Perceived security control Perceived privacy control Perceived integrity Perceived competence Distrust in E-commerce transactions Trust propensity Personality cultural environment experience Fig.2: Sulaiman's model [15] theoretical framework that can serve as the basis for further exploration of the role of persuasion in online consumer trust (See figure 3) Hsu s study (2007) elaborates two alternative modes of persuasion and explains which persuasive process is most effective for different personality types. It further presents a useful

6 / New Marketing Research Journal, Special Issue, May 2012 Central Website quality Customer service Perceived security Perceived Personality traits (sensing/intuit Peripheral variables Depth of information processes Cognitionbased trust Affect-based trust Trust Willingn ess to Dispositional trust Third party assurance Reputation Fig.3: Hsu s model [16] Srivinasan s study (2004) shows that Trust is one of many factors that contributes to e-business success. The key components of this trust model are the trusted seals, security and financial institutions (See figure 4). An established BAM with an e- business Financial institution Consumer trust Security E-business Trusted seal Trusted third party Fig.4: Srivinasan s model [17]

An ecrm model for banking industry In Iran / 7 Tan (2004) synthesis the viewpoints from across the disciplines and brings them together in a multi-dimensional trust model. From these multiple disciplines, three dimensions of trust emerged: dispositional trust, institutional trust and interpersonal trust, each bringing its own influencing factors into the overall intention to trust. From this model emerged the notion that the consumer as an individual is central to the understanding of trust, and in turn the individual s personality and culture form the foundation for the development of trust (See figure 5). Dispositional trust Extraversion Neuroticism Agreeableness Conscientiousness Openness to experience Culture Institutional trust Internal experience Perceived internet Protection Intenti on to tr st Online purcha se Interpersonal trust Competence Predictability Benevolence Integrity Fig.5: Tan's model [18] Laurn, Hsin-hui and Lin s study (2003) focused on the relationship between loyalty and trust. Their model shows the direct and indirect influences of customer satisfaction, trust, perceived value and attitudinal commitment on loyal behavior (repurchasing) (See figure 6). Trust Customer satisfactio Loyalty Commit Perceived value Fig.6: Laurn's ecrm model [19]

8 / New Marketing Research Journal, Special Issue, May 2012 Shankar, Sultan and Urban's study (2002) shows the antecedent factors could be classified into three broad groups: (1) Web site characteristics, (2) user characteristics, and (3) other factors. The other factors include such factors as the online medium and variables involving a combination of Web site and user characteristics. The consequences could be broadly grouped into three categories: (1) Intent to act, (2) Customer satisfaction and loyalty and (3) firm performance (See figure 7) Web site characteristics Navigation and user friendliness Advice Error-freeness Fulfillment Community Privacy (third part statement) Security (credit card protection) Trust seals & refund policies Brand Presentation Site longevity Selection of items Timeliness of information Links to other relevant sites Satisfaction & loyalty Problem resolution Satisfaction Commitment Repeat purchase Loyalty User characteristics Internet savvy Internet shopping behavior Entertainment experience Place of web usage Long term orientation Predisposition to technology Feeling of control Online trust Reliability / credibility Emotional comfort Quality Benevolence Firm performance Site traffic Prices Revenues Profitability / ROI Shareholder value Other characteristics Online medium Trustworthiness of firm Perceived size of firm Perceived reputation of firm Dependence on the firm Human service Communication Personalization Collaboration Intent to act Attitude toward firm online Perception of risk Willingness to buy Long-term orientation Fig.7: Shankar's model [20] In Callaghan and Shaw s study (2002), ethics is considered from two perspectives. The first one formalized ethics consists of the formal ethical policy of the organization as promoted through company policy, codes of ethics and codes of conduct. The second perspective, Individual Ethical Structure, consists of the moral and ethical values and beliefs of the organization s representative (See figure 8). Figure 9 also demonstrates direct and indirect relationship among the first perspective and the second with perceived trust.

An ecrm model for banking industry In Iran / 9 Formalized ethics Codes of ethics Codes of conduct Inculcation through induction, training, corporate policy & corporate culture Consumer awareness of organization s code of ethics & code of conduct Individual ethical structure Moral & ethical values/beliefs Perceived trust Consumer perception of trust in the relationship Behaviors reflective of individual ethical structure Fig.8: Callaghan's model [21] Cheung and Lee s trust model (2001) suggests that trust worthiness of e-vendor (consists of perceived security control, perceived privacy control, perceived competence and perceived integrity), external environment (consists of third party recognition and legal framework), propensity to trust and perceived risk affect the level of trust among consumers toward internet shopping or services(see figure 9) Trustworthiness on internet vendor Perceived security control Perceived privacy control Perceived integrity Perceived competence Internet shopping Propensity to trust External environment Third party recognition Legal framework Perceived risk Fig.9: Cheung's model [22] Research Methodology Research Factors and Measurement Trust Factor: Trust is not an easy value factor to measure. We envisage the need to translate it into a logical framework. In order to perform the analysis we focused on trusting beliefs. According to the literature (brought to

10 / New Marketing Research Journal, Special Issue, May 2012 you) in recent pages specially Bhattacherjee s suggestion (2002) and the outcomes of eight semi structured interviews with e-commerce experts in the relevant fields, we categorized trusting belief into 3 variables. Table 1 shows these variables and their measuring indicator components. Table 1: Trusting beliefs variables Trusting Beliefs Competence Competence Expertness Dynamism Benevolence Benevolence Goodwill Responsiveness Integrity Morality Integrity Credibility Reliability Dependability Satisfaction Factor: Wang et al. (2001) developed a 21-item measuring instrument to assess customer s satisfaction with a particular website that markets digital products and services. We picked up this measuring instrument. In our mentioned interviews we could also obtained agreement of the experts to apply this instrument for this part of the research. (See table 2). Furthermore, loyalty was taken simply as reusing the e-services. Table 2: the 21 Measurement instrument Satisfaction Customer support After-sale service Request understanding Rapid response Security Secure feeling Safe transaction User friendly Easy to use website Easy to read output E-services Adapted with need High quality services Payment system Transaction procedures Clear price information & transaction Fits your need

An ecrm model for banking industry In Iran / 11 Accurate information Reliable information Innovative service Up to date information The conceptual model After reviewing different definitions and models for ecrm and taking in mind in e-banking services, the research variables were selected and the conceptual modelis formed (showed in figure 10) Fig.10. the conceptual model The survey questionnaire We used a questionnaire with 60 items extending the concept of customer's trust and satisfaction. The responses were measured on a 7-point semantic differential scale with 1= strongly disagree, and 7 = strongly agree. Validity The structure and content of our questionnaire were discussed with the experts in mentioned semi-structured interviews and some modifications were made to justify the validity of our measuring instrument. To remove possible ambiguities a pilot test was carried out at a convenience sample of 20 e-banking service users and as the result some miner clarifications were made in necessary points of a number of questions. Reliability Data accumulated through questionnaires were run through the Cronbach alpha reliability test. Cronbach s alpha was used to test the reliability and validity of data. The results showed that all alpha scores were more than 0.76. This approves reliability of the data gathering instrument.

12 / New Marketing Research Journal, Special Issue, May 2012 The Population and Sampling Our population was defined as the collection of all e-banking service users in the city of Tehran as a clarifying and accessible sample of users in Iran. According to our estimation the size of population could be taken as unlimited. So we had to collect more than 411 completed questionnaires (according to Morgan table). Defining the procedure of sampling and relevant data gathering happened to be most difficult and time taking part of our study. We intended to distribute the survey packages and collecting completed ones via internet by a two way process of email and reply. From our interviews with experts (mentioned in earlier parts of this article), we knew, not to expect officially obtaining list of e-customers emails addresses. So, we planned to collect about a thousand e- customer s email addresses from branches of 14 different banks in Tehran, according to a cluster random sampling skim(each of these banks have branches and offer similar e-banking services in other areas of Iran). In the days of December 2009, we daily spent considerable amount of time in predetermined bank branches trying to talk to customers and getting the email addresses of those who were e-banking service users with some sort of their prior consent to reply our emails. Obtaining the addresses and emailing our survey package to each address were almost simultaneously taken place. The replies were begun to be received. The survey package contained a letter briefly and simply explaining our research aims, some hints to complete the questionnaire and a sentence of our sincere appreciation for those who help us by replying the filled questionnaire. The Hypotheses Based on key factors and relations derived from the literature and our semi structured interviews, our Hypotheses in the population of Tehran s e-banking service users (as a sample of e-customer in Iran) were formulated as follows: H1: Integrity is positively associated with benevolence. H2: Benevolence is positively associated with competence. H3: Integrity is positively associated with competence. H4: Trusting beliefs is positively associated with customer satisfaction. H5: Trusting beliefs is positively associated with customer loyalty. H6: Customer satisfaction is positively associated with customer loyalty. E-customer s Level of educations Demographic data showed that more than 72% of sample members had high school diploma or some sort of university education (see figure 11). This percentage may indicate, the majority of expletives had relatively enough knowledge to be familiar with the concept of key factors in this study Below high school diploma 18, high school diploma 37, above H.D. and university degrees 35, not mentioning 10.

An ecrm model for banking industry In Iran / 13 Fig.11: Educational Demography for the Sample Data Analysis: This research is a descriptive survey and uses the quantitative paradigm. To examine the hypothesis we used Spearman's rank correlation and Z test. The tests were conducted by SPSS version 11.5 for Windows operating system. The degree of significant assumed to be 5%. The trust score of each customer was calculated by averaging its measuring question scores obtained from the questionnaires (See figure 12). Then, satisfaction scores were calculated. After that, the Spearman's rank correlation for each component was calculated. The results indicate that benevolence, integrity and competence positively associate with together (See table 3). The satisfaction scores of each customer were calculated by summing all components scores and compare to trusting beliefs scores. The results indicate that trusting beliefs, satisfaction and loyalty positively associate with each other (See table 4). Table 3: the coefficient of correlation between trusting beliefs Components Benevolence Integrity Competence Benevolence 1.00 Integrity 0.67* 1.00 Competence 0.51* 0.58* 1.00 *The degree of significant is 5% in 2- tailed test Table 4: the coefficient of correlation between research variables Trusting Beliefs Satisfaction Loyalty Trusting Beliefs 1.00 Satisfaction o.53* 1.00 Loyalty 0.61* 0.62* 1.00 *The degree of significant is 5% in 2- tailed test

14 / New Marketing Research Journal, Special Issue, May 2012 Aggregate sample mean Fig. 12: Relative importance of trusting beliefs We used z-test for test the result in population, z-test be used because n was greater than 30. An example of null hypothesis shown in the illustration below: H 0 : ρ<=0 E (r s ) = 0 H 1 : ρ>0 V (r s ) = Our test value would place in acceptance region. The decision is that we cannot accept H 0 : ρ<=0 (α<5%). Based on the results, all of the hypotheses were accepted. The Model The research results demonstrate a model that shows relationships between satisfaction, trusting beliefs and loyalty. This model is based on e-banking service user s opinion in the city of Tehran. (See figure 13 where results of testing the model by R 2 is presented) Conclusion Trust is one of important factors contributing to e-business success. In Fig. 13: The Research ecrm Model this article, we developed a model to explain ecrm and its relations with other crucial factors in e-banking

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